OpenRaiser/Intern-Atlas huggingface dataset:
Phase-Priority based Directory Coherence for Multicore Processor
Venue: arXiv cs.AR 2013
Year: 2013

As the number of cores in a single chip increases, a typical implementation of coherence protocol adds significant hardware and complexity overhead. Besides, the performance of CMP system depends on the data access latency, which is highly affected by coherence protocol and on-chip interconnect. In this paper, we propose PPB (Phase-Priority Based) cache coherence protocol, an optimization of modern directory coherence protocol. We take advantage of the observation that transient states occur in directory coherence protocol, resulting in some unnecessary transient states and stalling. PPB cache coherence protocol decouples a coherence transaction and introduces the idea of phase message. This phase is considered as the priority of the message. Additionally, we also add new priority-based arbitrators in on-chip network to support PPB cache coherence protocol. This mechanism in on-chip network can support effective cache access, which makes the on-chip network more efficient. Our analysis on an execution-driven full system simulator using SPLASH-2 benchmark shows that PPB cache coherence outperforms a MESI based directory, and the number of unnecessary transient states and stalling reduces up to 24%. Also it reported the speedup of 7.4%. Other advantages of this strategy are reduced delay of flits and significantly less energy consumption in on-chip network.
Bioacoustical Periodic Pulse Train Signal Detection and Classification using Spectrogram Intensity Binarization and Energy Projection
Venue: arXiv cs.CV 2013
Year: 2013

The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work is to properly identify and detect minke songs from large multi-year datasets, this effort was developed using sounds off the coast of Massachusetts, in the Stellwagen Bank National Marine Sanctuary. The detection methodology is presented and evaluated on 232 continuous hours of acoustic recordings and a qualitative analysis of machine learning classifiers and their performance is described. The trained automatic detection and classification system is applied to 120 continuous hours, comprised of various challenges such as broadband and narrowband noises, low SNR, and other pulse train signatures. This automatic system achieves a TPR of 63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score of 71%.
Fault-tolerant control under controller-driven sampling using virtual actuator strategy
Venue: arXiv cs.SY 2013
Year: 2013

We present a new output feedback fault tolerant control strategy for continuous-time linear systems. The strategy combines a digital nominal controller under controller-driven (varying) sampling with virtual-actuator (VA)-based controller reconfiguration to compensate for actuator faults. In the proposed scheme, the controller controls both the plant and the sampling period, and performs controller reconfiguration by engaging in the loop the VA adapted to the diagnosed fault. The VA also operates under controller-driven sampling. Two independent objectives are considered: (a) closed-loop stability with setpoint tracking and (b) controller reconfiguration under faults. Our main contribution is to extend an existing VA-based controller reconfiguration strategy to systems under controller-driven sampling in such a way that if objective (a) is possible under controller-driven sampling (without VA) and objective (b) is possible under uniform sampling (without controller-driven sampling), then closed-loop stability and setpoint tracking will be preserved under both healthy and faulty operation for all possible sampling rate evolutions that may be selected by the controller.
Requirements Engineering of a Web Portal using Organizational Semiotics Artifacts and Participatory Practices
Venue: arXiv cs.SE 2013
Year: 2013

Software requirements are key elements that contribute to the quality and users satisfaction of the final system. In this work, Requirements Engineering (RE) of web sites is presented using an organizational semiotics perspective. They are shown as being part of an organization, with particular practices, rules and views considering stakeholders several differences and opinions. The main contribution of this paper is to relate an experience, from elicitation to validation, showing how organizational semiotics artifacts were exploited in a collaborative and participatory way to RE of a web portal. A case study is described in order to demonstrate the feasibility of using such artifacts to RE when we think about the system as being part of a social organization.
Augmenting Operating Systems With the GPU
Venue: arXiv cs.OS 2013
Year: 2013

The most popular heterogeneous many-core platform, the CPU+GPU combination, has received relatively little attention in operating systems research. This platform is already widely deployed: GPUs can be found, in some form, in most desktop and laptop PCs. Used for more than just graphics processing, modern GPUs have proved themselves versatile enough to be adapted to other applications as well. Though GPUs have strengths that can be exploited in systems software, this remains a largely untapped resource. We argue that augmenting the OS kernel with GPU computing power opens the door to a number of new opportunities. GPUs can be used to speed up some kernel functions, make other scale better, and make it feasible to bring some computation-heavy functionality into the kernel. We present our framework for using the GPU as a co-processor from an OS kernel, and demonstrate a prototype in Linux.
Analytical Evaluation of Coverage-Oriented Femtocell Network Deployment
Venue: arXiv cs.IT 2013
Year: 2013

This paper proposes a coverage-oriented femtocell network deployment scheme, in which the femtocell base stations (BSs) can decide whether to be active or inactive depending on their distances from the macrocell BSs. Specifically, as the areas close to the macrocell BSs already have satisfactory cellular coverage, the femtocell BSs located inside such areas are kept to be inactive. Thus, all the active femtocells are located in the poor macrocell coverage areas. Based on a stochastic geometric framework, the coverage probability can be analyzed with tractable results. Surprisingly, the results show that the proposed scheme, although with a lower defacto femtocell density, can achieve better coverage performance than that keeping all femtocells in the entire network to be active. The analytical results further identify the achievable optimal performance of the new scheme, which provides mobile operators a guideline for femtocell deployment and operation.
On differentially dissipative dynamical systems
Venue: arXiv cs.SY 2013
Year: 2013

Dissipativity is an essential concept of systems theory. The paper provides an extension of dissipativity, named differential dissipativity, by lifting storage functions and supply rates to the tangent bundle. Differential dissipativity is connected to incremental stability in the same way as dissipativity is connected to stability. It leads to a natural formulation of differential passivity when restricting to quadratic supply rates. The paper also shows that the interconnection of differentially passive systems is differentially passive, and provides preliminary examples of differentially passive electrical systems.
Utility Optimal Scheduling and Admission Control for Adaptive Video Streaming in Small Cell Networks
Venue: arXiv cs.IT 2013
Year: 2013

We consider the jointly optimal design of a transmission scheduling and admission control policy for adaptive video streaming over small cell networks. We formulate the problem as a dynamic network utility maximization and observe that it naturally decomposes into two subproblems: admission control and transmission scheduling. The resulting algorithms are simple and suitable for distributed implementation. The admission control decisions involve each user choosing the quality of the video chunk asked for download, based on the network congestion in its neighborhood. This form of admission control is compatible with the current video streaming technology based on the DASH protocol over TCP connections. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions for a small-cell network.
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) up-calls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate.
Sparse Adaptive Dirichlet-Multinomial-like Processes
Venue: arXiv cs.IT 2013
Year: 2013

Online estimation and modelling of i.i.d. data for short sequences over large or complex "alphabets" is a ubiquitous (sub)problem in machine learning, information theory, data compression, statistical language processing, and document analysis. The Dirichlet-Multinomial distribution (also called Polya urn scheme) and extensions thereof are widely applied for online i.i.d. estimation. Good a-priori choices for the parameters in this regime are difficult to obtain though. I derive an optimal adaptive choice for the main parameter via tight, data-dependent redundancy bounds for a related model. The 1-line recommendation is to set the 'total mass' = 'precision' = 'concentration' parameter to m/2ln[(n+1)/m], where n is the (past) sample size and m the number of different symbols observed (so far). The resulting estimator (i) is simple, (ii) online, (iii) fast, (iv) performs well for all m, small, middle and large, (v) is independent of the base alphabet size, (vi) non-occurring symbols induce no redundancy, (vii) the constant sequence has constant redundancy, (viii) symbols that appear only finitely often have bounded/constant contribution to the redundancy, (ix) is competitive with (slow) Bayesian mixing over all sub-alphabets.
The Karyotype Ontology: a computational representation for human cytogenetic patterns
Venue: arXiv cs.CE 2013
Year: 2013

The karyotype ontology describes the human chromosome complement as determined cytogenetically, and is designed as an initial step toward the goal of replacing the current system which is based on semantically meaningful strings. This ontology uses a novel, semi-programmatic methodology based around the tawny library to construct many classes rapidly. Here, we describe our use case, methodology and the event-based approach that we use to represent karyotypes. The ontology is available at http://www.purl.org/ontolink/karyotype/. The clojure code is available at http://code.google.com/p/karyotype-clj/.
Evolution of Covariance Functions for Gaussian Process Regression using Genetic Programming
Venue: arXiv cs.NE 2013
Year: 2013

In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared exponential covariance function is used as a default. However, this can lead to a misspecified model, which does not fit the data well. In the proposed approach we use a grammar for the composition of covariance functions and genetic programming to search over the space of sentences that can be derived from the grammar. We tested the proposed approach on synthetic data from two-dimensional test functions, and on the Mauna Loa CO2 time series. The results show, that our approach is feasible, finding covariance functions that perform much better than a default covariance function. For the CO2 data set a composite covariance function is found, that matches the performance of a hand-tuned covariance function.
Inferring the Origin Locations of Tweets with Quantitative Confidence
Venue: arXiv cs.SI 2013
Year: 2013

Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.
An Upper Bound on the Capacity of Vector Dirty Paper with Unknown Spin and Stretch
Venue: arXiv cs.IT 2013
Year: 2013

Dirty paper codes are a powerful tool for combating known interference. However, there is a significant difference between knowing the transmitted interference sequence and knowing the received interference sequence, especially when the channel modifying the interference is uncertain. We present an upper bound on the capacity of a compound vector dirty paper channel where although an additive Gaussian sequence is known to the transmitter, the channel matrix between the interferer and receiver is uncertain but known to lie within a bounded set. Our bound is tighter than previous bounds in the low-SIR regime for the scalar version of the compound dirty paper channel and employs a construction that focuses on the relationship between the dimension of the message-bearing signal and the dimension of the additive state sequence. Additionally, a bound on the high-SNR behavior of the system is established.
Computability vs. Nondeterministic and P vs. NP
Venue: arXiv cs.CC 2013
Year: 2013

This paper demonstrates the relativity of Computability and Nondeterministic; the nondeterministic is just Turing's undecidable Decision rather than the Nondeterministic Polynomial time. Based on analysis about TM, UM, DTM, NTM, Turing Reducible, beta-reduction, P-reducible, isomorph, tautology, semi-decidable, checking relation, the oracle and NP-completeness, etc., it reinterprets The Church-Turing Thesis that is equivalent of the Polynomial time and actual time; it redefines the NTM based on its undecidable set of its internal state. It comes to the conclusions: The P-reducible is misdirected from the Turing Reducible with its oracle; The NP-completeness is a reversal to The Church-Turing Thesis; The Cook-Levin theorem is an equipollent of two uncertains. This paper brings forth new concepts: NP (nondeterministic problem) and NP-algorithm (defined as the optimal algorithm to get the best fit approximation value of NP). P versus NP is the relativity of Computability and Nondeterministic, P/=NP. The NP-algorithm is effective approximate way to NP by TM.
Conditions for Convergence in Regularized Machine Learning Objectives
Venue: arXiv cs.LG 2013
Year: 2013

Analysis of the convergence rates of modern convex optimization algorithms can be achived through binary means: analysis of emperical convergence, or analysis of theoretical convergence. These two pathways of capturing information diverge in efficacy when moving to the world of distributed computing, due to the introduction of non-intuitive, non-linear slowdowns associated with broadcasting, and in some cases, gathering operations. Despite these nuances in the rates of convergence, we can still show the existence of convergence, and lower bounds for the rates. This paper will serve as a helpful cheat-sheet for machine learning practitioners encountering this problem class in the field.
Belief Propagation for Linear Programming
Venue: arXiv cs.AI 2013
Year: 2013

Belief Propagation (BP) is a popular, distributed heuristic for performing MAP computations in Graphical Models. BP can be interpreted, from a variational perspective, as minimizing the Bethe Free Energy (BFE). BP can also be used to solve a special class of Linear Programming (LP) problems. For this class of problems, MAP inference can be stated as an integer LP with an LP relaxation that coincides with minimization of the BFE at ``zero temperature". We generalize these prior results and establish a tight characterization of the LP problems that can be formulated as an equivalent LP relaxation of MAP inference. Moreover, we suggest an efficient, iterative annealing BP algorithm for solving this broader class of LP problems. We demonstrate the algorithm's performance on a set of weighted matching problems by using it as a cutting plane method to solve a sequence of LPs tightened by adding ``blossom'' inequalities.
On the Peculiarities of Design: An Engineering Perspective
Venue: arXiv cs.HC 2013
Year: 2013

It is a fact of our existence, that no matter where we are, we most often find ourselves either hearing, seeing, talking, or even engaged in design related activities. Despite this reality, the notion of 'design', and in particular 'engineering design', is often ambiguous, and at times obscure. Thus, the transfer of knowledge of this crucial topic to engineering students engaged in practical hands-on learning or analytical research is usually perplexing to accomplish. In light of this, it becomes worthwhile to dissect and reflect on the abstraction of the design process in engineering. In fact, the aim of this article is to investigate the facets of applied design, and elaborate on its diversity, complexity and elements. Eventually, by concretizing this subject matter, we hope to slightly assist engineering students in alleviating some of the vagueness associated with the principle of design, and enhance their technical skillset during innovative conceptions.
The state-of-the-art in web-scale semantic information processing for cloud computing
Venue: arXiv cs.DC 2013
Year: 2013

Based on integrated infrastructure of resource sharing and computing in distributed environment, cloud computing involves the provision of dynamically scalable and provides virtualized resources as services over the Internet. These applications also bring a large scale heterogeneous and distributed information which pose a great challenge in terms of the semantic ambiguity. It is critical for application services in cloud computing environment to provide users intelligent service and precise information. Semantic information processing can help users deal with semantic ambiguity and information overload efficiently through appropriate semantic models and semantic information processing technology. The semantic information processing have been successfully employed in many fields such as the knowledge representation, natural language understanding, intelligent web search, etc. The purpose of this report is to give an overview of existing technologies for semantic information processing in cloud computing environment, to propose a research direction for addressing distributed semantic reasoning and parallel semantic computing by exploiting semantic information newly available in cloud computing environment.
Key classification attack on block ciphers
Venue: arXiv cs.CR 2013
Year: 2013

In this paper, security analysis of block ciphers with key length greater than block length is proposed. When key length is significantly greater than block length and the statistical distribution of cipher system is like a uniform distribution, there are more than one key which map fixed input to fixed output. If a block cipher designed sufficiently random, it is expected that the key space can be classified into same classes. Using such classes of keys, our proposed algorithm would be able to recover the key of block cipher with complexity O(max(2^n, 2^{k-n}) where n is block length and k is key length. We applied our algorithm to 2- round KASUMI block cipher as sample block cipher by using weakness of functions that used in KASUMI.
Model Checking Techniques for Verification of an Encryption Scheme for Wireless Sensor Networks
Venue: arXiv cs.LO 2013
Year: 2013

In this paper, we deal with the formal verification of an encryption scheme for Wireless Sensor Networks (WSNs). Especially, we present our first results on building a framework dedicated to modelling and verification of WSNs aspects. To achieve our goal, we propose to specify WSNs models written in Petri nets using Promela constructs in order to verify correctness properties of them using SPIN Model checker. We first specify in Promela a Petri net description of an encryption scheme for WSNs that describes its behavior. Then, correctness properties that express requirements on the system's behavior are formulated in Linear Temporal Logic (LTL). Finally, SPIN model checker is used to check if a specific correctness property holds for the model, and, if not, to provide a counterexample: a computation that does not satisfy this property. This counterexample will help to detect the source of the eventual problem and to correct it.
Performance Evaluation of Enhanced Interior Gateway Routing Protocol in IPv6 Network
Venue: arXiv cs.NI 2013
Year: 2013

With the explosive growth in communication and network technologies, there is a great demand of IPv6 addressing scheme. However, the modern operating systems has option for this and with the development of IPv6 which removes the limitations imposed by IPv4 and provides the large number of address space. In this paper, authors have considered the Enhanced Interior Gateway Routing Protocol and presented a scenario for its performance evaluation in IPv6 networks and obtained results are highly considerable for the short distance of communication and don't represent any problem of performance degradation while sending or receiving the data.
Secure Cascade Channel Synthesis
Venue: arXiv cs.IT 2013
Year: 2013

We investigate channel synthesis in a cascade setting where nature provides an iid sequence $X^n$ at node 1. Node 1 can send a message at rate $R_1$ to node 2 and node 2 can send a message at rate $R_2$ to node 3. Additionally, all 3 nodes share bits of common randomness at rate $R_0$. We want to generate sequences $Y^n$ and $Z^n$ along nodes in the cascade such that $(X^n,Y^n,Z^n)$ appears to be appropriately correlated and iid even to an eavesdropper who is cognizant of the messages being sent. We characterize the optimal tradeoff between the amount of common randomness used and the required rates of communication. We also solve the problem for arbitrarily long cascades and provide an inner bound for cascade channel synthesis without an eavesdropper.